The Terminal Representation in Reinforcement Learning
Quick summary
arXiv:2605.31289v3 Announce Type: replace-cross Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representation (SR) and the default representation (DR). The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward. The DR builds on this by weighting trajectories with reward, integrating credit-assignment structure into the representation. Eigenvectors of both representations have been used to support a range of down
Key takeaways
- arXiv:2605.31289v3 Announce Type: replace-cross Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL).
- Two well established approaches are through the successor representation (SR) and the default representation (DR).
- The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward.
Why it matters
This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.
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